RAG-Driven-Generative-AI
Builds Retrieval Augmented Generation AI using LlamaIndex with support from Deep Lake and Pinecone
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
RAG-Driven-Generative-AI uses LlamaIndex with Deep Lake and Pinecone for retrieval augmentation, integrating OpenAI and Hugging Face models.
Good fit when
- When you need advanced RAG capabilities with LlamaIndex's specific toolset
- For projects that require tight integration of vector databases like Deep Lake and Pinecone
Avoid when
- If your project strictly requires customization beyond the offered models from OpenAI and Hugging Face
- When you prefer alternative database integrations not including Deep Lake or Pinecone
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (361d since push)
- As of Sep 19, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 19, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
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Install
git clone https://github.com/Denis2054/RAG-Driven-Generative-AIHow it fits your stack(1)
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Evidence and technical details
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Overview
The repository facilitates the development of Generative AI models enhanced by Retrieval Augmentation (RAG) through an integration of tools like LlamaIndex, Deep Lake, and Pinecone. It also leverages OpenAI and Hugging Face for generation and evaluation.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Sep 19, 2026
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README
<h1 align="center" RAG driven Generative AI, First Edition</h1 <p align="center" This is the code repository for <a href ="https://www.packtpub.com/en us/product/rag driven generative ai 9781836200918" RAG Driven GenAI, First Edition</a , published by Packt. </p <p align="center"...
For agents
This page has a .md twin and JSON over the API.